Search

Climate change in the Arctic is outpacing the global average and land-use is intensifying due to exploitation of previously inaccessible or unprofitable natural resources. A comprehensive understanding of how the joint effects of changing climate and productivity modify lake food web structure, biomass, trophic pyramid shape and abundance of physiologically essential biomolecules (omega-3 fatty acids) in the biotic community is lacking. We conducted a space-for-time study in 20 subarctic lakes spanning a climatic (+3.2 degrees C and precipitation: +30%) and chemical (dissolved organic carbon: +10 mg/L, total phosphorus: +45 mu g/L and total nitrogen: +1,000 mu g/L) gradient to test how temperature and productivity jointly affect the structure, biomass and community fatty acid content (eicosapentaenoic acid [EPA] and docosahexaenoic acid [DHA]) of whole food webs. Increasing temperature and productivity shifted lake communities towards dominance of warmer, murky-water-adapted taxa, with a general increase in the biomass of primary producers, and secondary and tertiary consumers, while primary invertebrate consumers did not show equally clear trends. This process altered various trophic pyramid structures towards an hour glass shape in the warmest and most productive lakes. Increasing temperature and productivity had negative fatty acid content trends (mg EPA + DHA/g dry weight) in primary producers and primary consumers, but not in secondary nor tertiary fish consumers. The massive biomass increment of fish led to increasing areal fatty acid content (kg EPA + DHA/ha) towards increasingly warmer, more productive lakes, but there were no significant trends in other trophic levels. Increasing temperature and productivity are shifting subarctic lake communities towards systems characterized by increasing dominance of cyanobacteria and cyprinid fish, although decreasing quality in terms of EPA + DHA content was observed only in phytoplankton, zooplankton and profundal benthos.

1. The concept of biodiversity-ecosystem functioning (BEF) has been studied over the last three decades using experiments, theoretical models, and more recently observational data. While theoretical models revealed that species richness is the best metric summarizing ecosystem functioning, it is clear that ecosystem function is explained by other variables besides species richness. Additionally, theoretical models rarely focus on more than one ecosystem function, limiting ecosystem functioning to biomass or production. There is a lack of theoretical background to verify how other components of biodiversity and species interactions support ecosystem functioning.2. Here, using simulations from a food web model based on a community assembly process and a trait-based approach, we test how species biodiversity, food web structure and predator-prey interactions determine several ecosystem functions (biomass, metabolism, production, and productivity).3. Our results demonstrate that the relationship between species richness and ecosystem functioning depends on the type of ecosystem function considered and the importance of diversity and food web structure differs across functions. Particularly, we show that dominance plays a major role in determining the level of biomass, and is at least as important as the number of species. We find that dominance occurs in the food web when species do not experience strong predation.4. By manipulating the structure of the food web, we show that species using a wider trait space (generalist communities) result in more connected food webs, and generally reach the same level of functioning with less species. The model shows the importance of generalist versus specialist communities on biodiversity-ecosystem functioning relationships, and as such, empirical studies should focus on quantifying the importance of diet/habitat use on ecosystem functioning.5. Our study provides a better understanding of BEF underlying mechanisms, and generates research hypotheses that can be considered and tested in observational studies. We recommend that studies investigating links between biodiversity and ecosystem functions should include metrics of dominance, species composition, trophic structure and possibly environmental trait space. We also advise that more effort should be made into calculating several ecosystem functions and properties with data from natural multi-trophic systems.

Host specificity of parasitoids may be measured by various specialization indices to assess the variation of interaction strength among species and the structure of the wider interaction network. However, the conclusions from analyses at the species and network levels may differ, which remains poorly explored. In addition, the recovery of cryptic species of hosts and parasitoids with molecular data may affect the structure of inferred interaction links. We quantified host specificity of hymenopteran parasitoids (family Encyrtidae) on armored scale insects (Hemiptera: Diaspididae) from a wide geographic sampling range across the Chinese Mainland based on both morphological and molecular species delimitation. Mitochondrial COI and nuclear 28S markers detected high cryptic species diversity in the encyrtids and to a lesser degree in the diaspidids, which divided generalist morphospecies into complexes of specialists and generalists. One‐to‐one reciprocal host–parasite links were increased in the molecular data set, but different quantitative species‐level indices produced contrasting estimates of specificity from various one‐to‐multiple and multiple‐to‐multiple host–parasite links. Network indices calculated from DNA‐based species, compared to morphology‐based species definitions, showed lower connectance and generality, but greater specialization and compartmentalization of the interaction network. We conclude that a high degree of cryptic species in host–parasitoid systems refines the true network structure and may cause us overestimating the stability of these interaction webs.

Anna Eklöf et al., How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small (< 10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure. © 2013 Blackwell Publishing Ltd/CNRS., AE and JCK supported by NSF EF # 0827493. AE also by SOEB. SA supported by DEB #1148867. BD supported by Carlsberg foundation and the Danish Council for Independent research. AMMG supported by Spanish Ministry of Education, BVA-2010-0845. PRG supported by FAPESP. MAP supported by CNPq. JMT supported by a Rutherford Discovery Fellowship, RSNZ. RCU, NPC, SBL and DPV supported by CONICET (PIP 2781 and 6564), FONCyT (PICT 1471 and 20805) and BBVA Foundation (BIOCON03-162). JB and AR supported by Spanish MICNN (CICYT CGL2005-00491) and CONSOLIDER (csd2008-0040)

How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure. Fil: Eklof, Anna. University of Chicago; Estados Unidos Fil: Jacob, Ute. Institute for Hydrobiology and Fisheries Science; Alemania Fil: Kopp, Jason. University of Chicago; Estados Unidos Fil: Bosch, Jordi. Universitat Autònoma de Barcelona; España Fil: Castro Urgal, Rocio. Consejo Superior de Investigaciones Cientificas. Instituto Mediterraneo de Estudios Avanzados; España Fil: Chacoff, Natacha Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina Fil: Dalsgaard, Bo. Universidad de Copenhagen; Dinamarca Fil: de Sassi, Claudio. University Of Canterbury; Nueva Zelanda Fil: Galetti, Mauro. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil Fil: Guimaraes, Paulo Roberto. Universidade de Sao Paulo; Brasil Fil: Lomascolo, Silvia Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina Fil: Martin Gonzalez, Ana M.. Universidad de Copenhagen; Dinamarca. Pacific Ecoinformatics and Computational Ecology Lab; Estados Unidos Fil: Pizo, Marco Aurelio. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil Fil: Rader, Romina. Stockholms Universitet; Suecia Fil: Rodrigo, Anselm. Universitat Autònoma de Barcelona; España Fil: Tylianakis, Jason M.. University Of Canterbury; Nueva Zelanda Fil: Vazquez, Diego P.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina Fil: Allesina, Stefano. University of Chicago; Estados Unidos